Code for Generalization of Clifford Data Regression to the Pauli Error Channel: A Pauli-Based Mitigation Approach

This repository contains the necessary Python code to reproduce the results presented in the paper:

“Generalization of Clifford Data Regression to the Pauli Error Channel: A Pauli-Based Mitigation Approach”
Francesc Sabater and Carlos A. Riofrío

The work explores and compares two learning-based quantum error mitigation techniques:

• Direct Clifford Data Regression (CDR)
• Pauli-based CDR, a generalization that applies mitigation on decomposed Pauli terms, achieving higher accuracy under realistic noise models.

The repository is organized into two folders:

1. VQE/ – Contains scripts and workflows for Variational Quantum Eigensolver (VQE) simulations and mitigation.
2. QAOA/ – Contains scripts for Quantum Approximate Optimization Algorithm (QAOA) studies and histogram mitigation.

Each folder includes its own README.md with detailed instructions on how to execute the simulations, train mitigation models, and generate figures corresponding to the paper’s results.

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How to Reproduce the Results

To reproduce the numerical and hardware results presented in the paper follow the step-by-step workflows described in VQE/README.md and QAOA/README.md.

The datasets and figures produced will reproduce the results shown in the paper.

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Reference

If you use this repository or its code, please cite: (paper to be published soon)

License

This repository is licensed under the Apache License, Version 2.0.
You may obtain a copy of the license at:

http://www.apache.org/licenses/LICENSE-2.0

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